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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 09, SEPTEMBER 2019 ISSN 2277-8616
Affine Moment Invariant Based Offline Tamil
Handwritten Character Recognition Using Artificial
Neural Networks
Dr.R.Athilakshmi, R.Priyadharsini
Abstract: Hand written character recognition is widely used in many applications .For Tamil character recognition quite a few work has been reported in
the literature. Affine transformations are composites of some basic transformations. In this paper we proposed a method of feature extraction using
affine moment invariant for affine transformed character objects. Six different transformations are applied and the affine moment invariants features are
extracted, trained and tested using Back propagation network. Due to the variations, size, skew and slight rotation present in the structure of the
character object, affine moment Invariant proves better results for character recognition.
Index Terms: Affine transformation, Affine moment Invariant, Affine shear Rotation, Back propagation network, Image Processing, Robust feature
extraction, Tamil character Recognition.
—————————— ——————————
1. INTRODUCTION
Handwritten character recognition is one of the most
challenging topics in pattern recognition. It is widely used in
many applications such as Translation, Keyword
recognition, Signboard Translation, Text-to-Speech
Conversion and Image scene analysis etc. Lots of work has
been done on European and Arabic (Urdu) Punjabi, Bangla,
Tamil, and Gujarati etc. are very less explored due to
limited usage Ayush Purohit and Shardul Singh Chauhan
[8]. Tamil is one of the oldest languages in the world with
rich literature. Tamil language script is different from other
Indian languages. It has got 12 vowels, 18 consonants and
6 special characters, a set of 262 alphabets exists in the Fig. 1. Different writing styles
Tamil script. Each person has a distinctive style of writing.
Some people have handwritings that are difficult to 2. LITERATURE REVIEW
recognize the characters. Robust feature extraction is very Affine Moment Invariant is applied in character recognition.
important to improve the performance of character It is mostly used to recognize the object of the invariance
recognition that concentrates on the problem of different characteristics of the image. Quite a few work has been
writing styles and a non-uniform slant. In general, skew, reported in the literature for character recognition using
slant, the skew angle, the slant angle and the position of affine moment invariants. Initially, John Fusser & Thomas
baseline are determined in the text lines for character suk [4] constructed affine moment invariant based on
recognition. A collection of Tamil alphabets and words with algebraic theory of invariants, they developed a new tool for
regard to different writing styles are given as samples in character recognition in 1994 independent of the character
Fig. 1. size and variations. Mohamed Abaynarh and Lahbib
Zenkouar [1] has presented the amazing character
recognition using Legendre moment features. A general
theorem by Yuanbin Wang [2] to construct the affine
invariants consisting of the extended geometric moments
under affine transform is presented. Affine moment
invariant used for human activity recognition in Samy Sadek
[3]. A general framework for affine moment invariants and
affine moment descriptors are also derived, by Janne
Haiikia [5]. For Tamil hand written character recognition, no
work has been reported in the literature using affine
transform. Chain-coded stroke contours are used as feature
descriptors for Tamil script recognition in Rajkumar and
___________________________________
Bahraini [6].In the paper, dhanyl [7] filter based method was
proposed to extract Tamil characters present in multilingual
Dr.R.Athilakshmi, Doctorate, Associate Professor, Department of documents. In paper [7], two approaches i.e. spatial
Computer Technology at Sri Krishna Arts and Science College, features and Gabor filter were compared where Gabor filter
India, (E-mail: athilakshmir@skasc.ac.in)). representing orientation and frequency observed to
R.Priyadharsini, Research Scholar, Assistant Professor, Department
of Computer Applications at Sri Krishna Arts and Science possess good discriminating capability. Another moment
Cillege,India, (E-mail: priyadharsinir@skasc.ac.in)). based descriptor combined with density based descriptor to
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 09, SEPTEMBER 2019 ISSN 2277-8616
increase the recognition accuracy of devanagri script image should be converted to black and white image.
proposed by R. Bajaj, L. Dey, and S. Chaudhari [9]. Sample dataset is shown in Fig. 3. All the 247 Tamil
alphabets are individually captured to store in database.
3. AFFINE MOMENT INVARIANT The preprocessing step is required to normalize strokes
Invariants of geometric moments with respect to affine and variations present in the text. These variations or
transformations are generally called affine moment distortions are caused by the irregular size of text, missing
invariants. Many researchers have contributed to the points during pen movement collections, jitter present in
development of affine moment invariants. The concept of text, left or right bend in handwriting and uneven distances
moments of images into the pattern recognition field was of points from neighboring positions. The conversion of the
introduced by Hu in 1962. He presented a fundamental grayscale image to black and white is called binarization. In
theorem of affine invariants in his paper. He presented a the conversion, it is possible to set threshold values. If the
fundamental theorem of affine invariants in his paper. intensity values are higher than the threshold, they are
Different mathematical tools were used by different considered white and the values which are lower than the
research groups to derive moment invariants. At first, only a threshold are considered black. The process of changing
few affine moment invariants were published. Based on the intensity value of the pixel to the range [0, 1] and the
classical algebraic invariant theory, Flusser and Suk[2] conversion of various dimension images into fixed
derived a set of four affine moment invariants. dimensions is called as normalization. The matrix values of
the image can be normalized along the column and row
using the normc and normr commands in Matlab.
(
(2)
(4)Where is given in equation The geometric
moments of order (p, q) of an image f(x, y) are defined
Figure 3.Sample Dataset
by (5)Where p and
q are nonnegative integers. If f(x, y) is piecewise continuous Fig. 2. Sample Dataset
and has nonzero values only in a finite domain, moments of
all orders exist. The central moments are defined as The character image is divided into mxn image zones . To
obtain the local characteristic of an image, the features are
extracted from each zone to form the feature vector. The
Where , (6)The complex moments of input image is resized to spatial resolution of 128x128,
which is then divided into 64 zones of 16x16 pixels each.
order (p, q) of an image f(x, y) are defined as
And from each zone, four affine moment invariants were
(7) extracted, yielding 256features per image.
Flusser has also constructed a general method to rotational
invariants of images based on complex moments [3]. Let n 4. AFFINE TRANSFORMATIONS
≥ 1 and let ki, pi, and qi (i = 1 . . . n) be nonnegative Affine transformations are composites of four basic types of
transformations: translation, rotation, scaling (uniform and
non-uniform), and shearing. Affine transformations do not
necessarily preserve either distances or angles, but affine
integers such that transformations map straight lines to straight lines and
(8) Then is rotational invariant. affine transformations preserve ratios of distances along the
Translation invariance is obtained by using central complex
moments. Scaling invariance can be achieved by the same
normalization proposed by Hu [10].
3. SYSTEM DESCRIPTION
The input image taken through camera or some scanner.
The input captured may be in gray color or binary from
scanner or digital camera in JPEG format. First, the original
RGB image has to be converted to grayscale and then the
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 09, SEPTEMBER 2019 ISSN 2277-8616
TABLE 1
CLASSIFICATION RESULTS OF AFFINE MOMENT
INVARIANT FOR TAMIL CHARACTER DATASET
In- Recognition
Method Used Correct (70) correct Accuracy
(%)
BPN+ Affine 66 4 94
X Shear
BPN+ Affine 65 5 93
Y Shear
BPN+ Rotate 63 7 90
Left 30
BPN+ Rotate 65 5 93
Right 30
BPN+
Fig. 3. Affine Transformed image Horizontal 63 7 90
Stretch
straight lines. Six different transformations were BPN+ vertical 64 6 91
demonstrated with respect to affine shearing, affine rotation Stretch
and affine stretching. BPN+ English
Alphabets,
numbers, 56 14 80
4. RESULTS AND DISCUSSIONS symbols.
The performance of the proposed method was evaluated
with offline handwritten images. For the experiment we took TABLE II: TEST RESULTS OF INV1 ON THE FIRST 18
247 gray scale images of Tamil characters, resolution 128 × IMAGES
128, and used them to train a back propagation classifier
for each tested method. The test arrangements and the Trans
results of the experiments are described in the following Letter Trans 1 .2 Trans.3 Trans.4 Trans.5 Trans.6
subsections. The images were first preprocessed by the 5.5E-10 5.5E- 5.5E-10 5.5E-10 5.5E-10 5.5E-10
10
binarization method using MATLAB’s function. After 5.6 E-10 5.6 E- 5.6 E-10 5.6 E-10 5.6 E-10 5.6 E-10
processing the data, binary object is divided into fixed 10
number of zones for feature extraction. The extracted 4.2E-10 4.2E- 4.2E-10 4.2E-10 4.2E-10 4.2E-10
features are stored in a separate array for each object. For 10
4.4E-10 4.4E- 4.4E-10 4.4E-10 4.4E-10 4.4E-10
testing, the object were transformed based on the 10
estimated parameters of affine transform. The resulting 3.3E-10 3.3E- 3.3E-10 3.3E-10 3.3E-10 3.3E-10
images are shown in Fig. 3. For each experiment, image 10
transform based on the affine moment descriptors was 5.3E-10 5.3E- 5.3E-10 5.3E-10 5.3E-10 5.3E-10
carried out for a set of deformed images. These images 10
were preprocessed in the same way as in the previous 5.5E-10 5.5E- 5.5E-10 5.5E-10 5.5E-10 5.5E-10
experiment. The invariant moments calculated for first 18 10
5.2 E-10 5.2 E- 5.2 E-10 5.2 E-10 5.2 E-10 5.2 E-10
character images are shown in table. Then the classification 10
performance was estimated using these same images 3.5 E-10 3.5 E- 3.5 E-10 3.5 E-10 3.5 E-10 3.5 E-10
10
disturbed by a six different affine transformations is shown 3.6 E-10 3.6 E- 3.6 E-10 3.6 E-10 3.6 E-10 3.6 E-10
in Table I. To demonstrate the invariance of the AMIs, six 10
3.7 E-10 3.7 E- 3.7 E-10 3.7 E-10 3.7 E-10 3.7 E-10
affine transformations were performed for each of the test 10
images. The affine distortions of the images are depicted in 2.6 E-10 2.6 E- 2.6 E-10 2.6 E-10 2.6 E-10 2.6 E-10
10
Fig 4. They are transformed images of the second test 4.7 E-10 4.7 E- 4.7 E-10 4.7 E-10 4.7 E-10 4.7 E-10
image. All invariants of the type Inv1, Inv2, Inv3, and Inv4 10
4.8 E-10 4.8 E- 4.8 E-10 4.8 E-10 4.8 E-10 4.8 E-10
had been tested by using equations (1) to (4). As the 10
complete test results are too huge to include in this paper, 4.2 E-10 4.2 E- 4.2 E-10 4.2 E-10 4.2 E-10 4.2 E-10
10
the test results of the only the first two invariants has been 4.3 E-10 4.3 E- 4.3 E-10 4.3 E-10 4.3 E-10 4.3 E-10
presented on the first 18 test images in Table II and Table 10
5.1 E-10 5.1 E- 5.1 E-10 5.1 E-10 5.1 E-10 5.1 E-10
III. 10
5.2 E-10 5.2 E- 5.2 E-10 5.2 E-10 5.2 E-10 5.2 E-10
10
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INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 8, ISSUE 09, SEPTEMBER 2019 ISSN 2277-8616
TABLE III: [6] S RajaKumar, Dr. V. Subbiah Bharathi,―Ancient
TEST RESULTS OF INV2 ON THE FIRST 18 IMAGES tamil script recognition from stone inscriptions
using slant removal method‖, International
Letter Trans 1 Trans.2 Trans.3 Trans.4 Trans.5 Trans.6 Conference on Electrical, Electronics and
Biomedical(Malaysia) May 19-20, 2012.D Dhanya,
3.8E- 3.8E- 3.8E-10 3.8E-10 3.7E-10 3.8E-10
10 10 A G Ramakrishnan and Peeta Basa Pati, “Script
3.6 E- 3.6 E-
10 10 3.6 E-10 3.6 E-10 3.6 E-10 3.6 E-10 identification in printed bilingual documents‖,
3.1E- 3.1E- Sadhana, Vol. 27, Part 1, February 2002, pp. 73–
10 10 3.1E-10 3.2E-10 3.1E-10 3.1E-10 82
3.4E- 3.4E-
10 10 3.4E-10 3.3E-10 3.4E-10 3.4E-10 [7] Ayush Purohit and Shardul Singh Chauhan, ―A
2.1E- 2.1E-
10 10 2.1E-10 2.1E-10 1.9E-10 2.1E-10 literature survey on handwritten character
3.3E- 3.3E- recognition‖, International Journal of Computer
10 10 3.3E-10 3.2E-10 3.3E-10 3.3E-10
3.2E- 3.2E- Science and Information Technologies, Vol. 7 (1) ,
10 10 3.2E-10 3.2E-10 3.1E-10 3.2E-10 2016, 1-5, 1ssn :0975-9646.
3.5 E- 3.5 E-
10 10 3.5 E-10 3.4 E-10 3.5 E-10 3.5 E-10 [8] Reena Bajaj, Lipika Dey, and
1.3 E- 1.3 E-
10 10 1.3 E-10 1.3 E-10 1.3 E-10 1.3 E-10 S.Chaudhury,―Devnagari numeral recognition by
combining decision of multiple connectionist
1.5 E- 1.5 E- 1.5 E-10 1.5 E-10 1.4 E-10 1.5 E-10
10 10 classifiers‖, Sadhana, Vol.27, part. 1, pp.-59-72,
1.7 E- 1.7 E- 1.7 E-10 1.6 E-10 1.7 E-10 1.7 E-10
10 10 2002.
1.6 E- 1.6 E- 1.6 E-10 1.6 E-10 1.6 E-10 1.6 E-10 [9] MK Hu,‖Visual pattern recognition by moment
10 10
1.4 E- 1.4 E- invarints‖, IRE transactions on Image theory,
10 10 1.4 E-10 1.4 E-10 1.4 E-10 1.4 E-10 Febrauary,1962.
1.8 E- 1.8 E-
10 10 1.8 E-10 1.8 E-10 1.8 E-10 1.8 E-10
2.2 E- 2.2 E-
10 10 2.1 E-10 2.2 E-10 2.3 E-10 2.2 E-10
2.3 E- 2.3 E-
10 10 2.3 E-10 2.4 E-10 2.3 E-10 2.3 E-10
4.1 E- 4.1 E-
10 10 4.1 E-10 4.2 E-10 4.2 E-10 4.1 E-10
4.2 E- 4.2 E-
10 10 4.2 E-10 4.1 E-10 4.2 E-104 .2 E-10
5. CONCLUSION
A moment based method for matching image objects under
affine transformation was proposed. The method is based
on the second and the third order moments of the image
objects. The descriptors obtained are called affine moment
descriptors are explored for offline handwritten Tamil
character recognition. The results of all six transformations
have been presented. The results clearly shows that a
recognition system based on affine shear and rotations of
Tamil characters performs far better than the traditional
English alphabet and number based classifier.
REFERENCES
[1] Mohamed Abaynarh and Lahbib Zenkouar, ―Offline
handwritten characters recognition using moments
features and neural networks‖, Computer
Technology and Application 6 (2015) , 19-29
[2] Yuanbin Wang, Xingwei Wang, Bin Zhang, and
Ying Wang, ―A novel form of affine moment
invariants of grayscale images‖, Elektronika Ir
Elektrotechnika, Issn 1392-1215, Vol. 19, No. 1,
2013.
[3] Samy Sadek, Ayoub Al-Hamadi, Gerald Krell, and
Bernd Michaelis, ―Affine- invariant feature
extraction for activity recognition‖, Volume
2013, Article ID 215195
[4] John Flusser and Thomas Suk , ―Affine invariants:
a new tool for character recognition‖, Pattern
Recognition Letters, Volume 15, Issue 4, April
1994, Pages 433- 3 6.
[5] Janne Heikkila, ―Pattern matching with affine
momentdescriptors‖, Elsevier Science, March
2004.
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